Privacy centric feature analysis for mobile devices

ABSTRACT

Privacy centric feature analysis. A secure set of multiple mapped features is selected and provided to a mobile device. Each mapped feature maps a sharable feature to a matching criterion for an item of protected information and no combination of mapped features for a secure set are unique to an individual item of protected information. Privacy compliance instructions enable the mobile device to select a mapped feature from a received set of mapped features by identifying an item of protected information available to the mobile device which corresponds to a matching criterion found in the received set of mapped features. The sharable feature of the selected mapped feature is identified and sent to a privacy compliant destination. Advantageously, the analysis system protects the privacy of the mobile device user because it does not require the mobile device to relay protected information for the selection of customized content or relevant advertisements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional applicationSer. No. 15/048,987 entitled “Privacy Centric Feature Analysis forMobile Devices”, by Feldman et al., filed on Feb. 19, 2016, which is acontinuation of U.S. Non-Provisional application Ser. No. 14/826,618entitled “Privacy Centric Feature Analysis for Mobile Devices”, byFeldman et al., filed on Aug. 14, 2015, now U.S. Pat. No. 9,313,656issued on Apr. 12, 2016, which is a Continuation of U.S. Non-Provisionalapplication Ser. No. 13/937,103 entitled “Privacy Centric FeatureAnalysis for Mobile Devices”, by Feldman et al., filed on Jul. 8, 2013now U.S. Pat. No. 9,143,932 issued on Sep. 22, 2015, which claimspriority from U.S. Provisional Application No. 61/791,845 entitled“Privacy Centric Feature Analysis for Mobile Devices”, by Feldman etal., filed on Mar. 15, 2013, all of which are hereby incorporated byreference in their entirety.

BACKGROUND

Field of Invention

The invention pertains in general to networked advertising and inparticular to methods of protecting the privacy of mobile device users.

Description of Related Art

Information provided by mobile devices can be used for a number ofpurposes, such as understanding the way consumers interact with mobiledevices and mobile applications, the selection of custom content andadvertising targeting. However, consumers are rightfully concerned aboutprivacy issues related to the types of information which may becollected and where that information may be transmitted.

Collecting the exact geolocation of a mobile device is just one exampleof the type of information collection which may make peopleunderstandably uncomfortable. What is needed is a method for enabling amobile device to transmit enough information so that an analysis systemcan perform operations such as audience analysis, reporting, contentcustomization and selecting advertisements for delivery to the mobiledevice, without compromising the privacy of the mobile device user.

SUMMARY

Embodiments of the invention provide a method, a non-transitorycomputer-readable storage medium and a system for the privacy centricfeature analysis for mobile devices. In an embodiment, a secure set oftwo or more mapped features are selected for provision to a mobiledevice. A mapped feature maps a sharable feature to a matching criterionfor an item of protected information. The mapped features of a secureset are selected such that no combination of the sharable features in asecure set are unique to an item of protected information.Advantageously, by careful selection of the mapped features of a secureset, the feature analysis system prevents the accidental collection ofprotected information.

In an embodiment, privacy compliance instructions are provided to themobile device, for execution at the mobile device. The privacycompliance instructions direct the mobile device to select a mappedfeature from a received secure set of mapped features by identifying anitem of protected information available to the mobile device whichcorresponds to a matching criterion found in the secure set. The privacycompliance instructions direct the mobile device to identify a sharablefeature of the selected mapped feature and send the sharable feature toa privacy compliant destination.

In an embodiment, at the privacy compliant destination, such as theanalysis system or a third-party privacy compliant system, a sharablefeature received from a mobile device may be used to perform subsequentanalysis and actions such as, but not limited to, the selection ofcustomized content for the mobile device, the selection of anadvertisement for delivery to the mobile device, reporting, analysis andmodeling. Advantageously, the mapped features may be designed to protectthe privacy of the mobile device user by careful designation of theitems of protected information and the careful selection of the sharablefeatures.

In an embodiment, the feature analysis system may receive a responsefrom a mobile device, with the response comprising one or more sharablefeatures. The feature analysis system may select a new secure set ofmapped featured for provision to the mobile device based on the presenceor absence of a particular sharable feature in the response.

BRIEF DESCRIPTION OF DRAWINGS

Figure (FIG. 1 illustrates an example computing environment inaccordance with an embodiment;

FIG. 2a illustrates an example of a list of mapped features;

FIG. 2b illustrates an example of a hierarchy of information related toa mapped feature and protected information, under a particular privacypolicy, according to an embodiment;

FIG. 2c illustrates an example of a list of mapped features;

FIG. 2d illustrates an example of a list of mapped features;

FIG. 3a illustrates an example of possible interactions between afeature analysis system, a mobile device and a privacy compliant system,according to an embodiment.

FIG. 3b illustrates an example of possible interactions between afeature analysis system, a mobile device, a resource system and aprivacy compliant system, according to an embodiment.

FIG. 4 is a high-level block diagram illustrating an example of acomputer for use as a feature analysis system, a mobile device, aresource system and/or a privacy compliant system of FIG. 1, FIG. 3a andFIG. 3 b;

FIG. 5 illustrates is a flow chart illustrating an example of a methodfor the privacy centric feature analysis for mobile devices, inaccordance with an embodiment; and

FIG. 6 illustrates is a flow chart illustrating an example of a methodfor the privacy centric feature analysis for mobile devices, inaccordance with an embodiment.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates an example computing environment in accordance withan embodiment. In particular, feature analysis system 100 can be used tohelp understand the features which are relevant to a mobile devicewithout the collection of protected information, such as exactgeolocation data, from the mobile device. Instead of collectingprotected information from a mobile device at the analysis system, thefeature analysis system provides the mobile device with privacycompliance instructions and mapped features which enable the mobiledevice to map protected information available to the mobile device, suchas an exact geolocation of a mobile device, into a sharable feature,such as “near a beach resort”, which may be shared with the featureanalysis system, one or more privacy compliant systems or combinationsthereof. By careful selection of mapped features at the feature analysissystem, mapping between a first type of information, such as an exactgeolocation, and a second type of information, such as a descriptioncommon to multiple non-contiguous geographic locations, can enable themobile device to share useful information without compromising consumerprivacy. Advantageously, the sharable features can be used to select amobile device to receive customized content or advertisements withoutcompromising the privacy of the mobile device user.

As shown in FIG. 1, the computing environment includes a featureanalysis system 100, a mobile device 120, a resource system 130 and aprivacy compliant system 140. The feature analysis system 100, themobile device 120, the resource system 130 and the privacy compliantsystem 140 are connected by a communication network 150, such as a localarea network, a wide area network, a wireless network, an intranet, acable network, a satellite network, a cellular phone network, an opticalnetwork, the Internet or combinations thereof.

In various embodiments, mobile device 120 receives and transmits dataover the communication network 150. Examples of a mobile device 120include, but are not limited to, a smartphone, mobile computer, laptop,computing tablet, personal digital assistant (PDA), portable gamingdevice, e-reading device and a cell phone. Although only one mobiledevice 120 is shown in FIG. 1 for clarity, any number of mobile devicesmay be connected to the communication network 150.

One or more mobile applications 122 may be installed on and operate on amobile device 120. Examples of mobile applications include, but are notlimited to, an internet browser, games, information retrievalapplications and general productivity applications. Some mobileapplications can detect or receive information from the mobile devicehardware, mobile device operating system, mobile device firmware,systems integrated with the mobile device, input from the mobile deviceoperator or combinations thereof. For example, a mobile application mayhave access to a status, a hardware identifier, a software identifier,or combinations thereof, accessed through a mobile operating system orfirmware. A mobile application or a mobile device may have access togeolocation information accessed through a GPS (Global PositioningSystem) system integrated with or coupled to the mobile device.Geographic location may be accessed or inferred by a mobile applicationor mobile device from other sources instead of or in addition to GPSdata, such as, but not limited to, information provided by a mobiledevice operator, triangulation from cell phone towers and accelerometerdata.

Mobile device 120 can be associated with an identifier. For example, ahardware device identifier such as a Media Access Control Address (MACaddress), International Mobile Station Equipment Identity (IMEI), MobileEquipment Identifier (MEID) or Electronic Serial Number (ESN) can bestored on a device. A software identifier such as a cookie value, anapplication identifier, an application installation identifier or anoperating system identifier such as an Android_ID and UDID can be storedlocally on a mobile device. In some cases, an identifier value can behashed for use by a feature analysis system 100 to produce an anonymous,non-reidentifiable identifier for the mobile device, providing anadditional layer of privacy. In another embodiment, identifiers can bepartially or wholly composed and/or stored remotely from a mobiledevice. In an example, identifiers do not comprise personallyidentifiable information (PII) such as a person's name or streetaddress.

In some cases, a mobile device or a mobile application can be associatedwith a non-unique identifier. For example, a cohort of mobile deviceswith a common feature such as a device type, device model, devicemanufacturer, device feature capability, application version,application installation date, application manufacturer or applicationtype may share a non-unique identifier. Information can be collected inconjunction with this non-unique identifier in order to preserve privacyfor the consumer while providing useful information to marketers.

A resource system 130 receives requests for information from a mobiledevice 120 and makes information available to the mobile device 120. Forexample, a resource system 130 may receive a request from a mobiledevice 120 which includes a geolocation expressed in latitude andlongitude, and return information to the mobile device, such as a streetaddress which corresponds to the received latitude and longitude.Another example of a resource system may receive a street address andreturn a geolocation expressed in latitude and longitude. Anotherexample of a resource system may receive geolocation information from amobile device and return information, such as demographic orsociographic information, which correlates with the receivedgeolocation. In an example, a resource system 130 can receive a requestfor information from a mobile device which does not include informationrelated to the mobile device's geolocation, and provide informationwhich correlates with the received information.

In an embodiment, the feature analysis system 100 includes a policymanager 102, a mapping manager 104, a mapped feature selector 106, aninstruction manager 108 and a data repository 110. Those of skill in theart will recognize that other embodiments of the feature analysis system100 can include additional and/or different modules than the ones shownin FIG. 1. Moreover, the functionalities can be distributed among themodules in a different manner than described here.

A privacy policy comprises one or more protected information rules whichcan be used to differentiate between protected information (which shouldnot be transmitted from a mobile device to a privacy compliant system)and sharable features (which may be transmitted from a mobile device toa privacy compliant system under the proper circumstances). In variousembodiments, a privacy policy may apply to one or more individual mobiledevices, one or more individual instances of an installed mobileapplication, one or more installation cohorts of a mobile application,or combinations thereof. In some cases, a single global privacy policymay be implemented for the feature analysis system 100. In some cases, aflat or hierarchical privacy policy may be implemented, with protectedinformation rules configured by one or more entities, such as, but notlimited to, mobile device operators, mobile device vendors, mobileapplication concerns, feature analysis system operators, privacycompliant system operators, mobile software application vendors, mobilesoftware application developers, mobile software applicationdistributors and mobile device software-as-a-service (SAAS) providers.In an embodiment, policy manager 102 manages the privacy policies. Insome cases, one or more protected information rules may be maintained atthe mobile device and applied to information sent from the mobile deviceto privacy compliant systems.

In an example, a protected information rule can be used to characterizeinformation which is permitted to be sent from the mobile device to aprivacy compliant destination such as the feature analysis system 100, aprivacy compliant system 140 or both. For example a protectedinformation rule can comprise a white list which identifies the sharablefeatures which may be sent, which privacy compliant destinations asharable feature may be sent to, or combinations thereof. For example, amobile application vendor may establish a protected information rulewhich explicitly permits the mobile application to send theinternational mobile country code (a specific sharable feature in thisexample) to the feature analysis system 100 (a specific privacycompliant destination in this example).

In an example, a protected information rule can characterize informationwhich is not permitted to be sent from the mobile device to adestination such as a privacy compliant destination, the featureanalysis system 100 a privacy compliant system 140 or combinationsthereof. For example a protected information rule can comprise a blacklist which identifies the protected information which may not be sent,prohibited destinations for protected information, or combinationsthereof. For example, a single global privacy policy may be implementedfor the feature analysis system 100 which prohibits the transmission ofexact geolocation data from a mobile device to any destination.

Mapping manager 104 manages mapped features for the feature analysissystem 100. A mapped feature maps one or more potentially sharablefeatures to one or more matching criterion for an item of protectedinformation. One or more mappings may be configured for the featureanalysis system by an operator, provided by an external system,assembled by the feature analysis system or combinations thereof. In anexample, the mappings may be generated based on data sources such as,but not limited to, US Census Data, marketing research and consumerhistories of online behaviors.

The feature analysis system 100 can generate one or more mappedfeatures, receive one or more mapped features or combinations thereof.FIG. 2a illustrates an example of a list of mapped features. In thisexample, the current location of a mobile device, as described by a cityname, has been designated an item of protected information; the cityname may not be transmitted from the mobile device to the featureanalysis system 100. However, some features of the current location ofthe mobile device are sharable features. In this case, the sharablefeatures (“high density urban area”, “coastal area”, “suburban area” and“rural area”) are non-unique because knowledge of the feature does notuniquely identify the location of the mobile device.

FIG. 2b illustrates an example of a hierarchy of information related toa mapped feature and protected information, under a particular privacypolicy, according to an embodiment. A feature of a city is a featurewhich may describe multiple different cities, such as population density(sparse, moderate, dense), climate zone (tropical, desert, sub-tropical,mesothermal, etc) and a per capita income range. The hierarchy is listedin order of increasing level of specificity from the least specific (afeature of a city 221 is shared by every location in the city, and canbe shared by other cities too) to the most specific (precise latitudeand longitude 224 describes a highly accurate position). The designationof “non-unique sharable” or “protected” is derived from a particularprivacy policy, so the designations for a hierarchy reflect theprotected information rules of a particular privacy policy. Note alsothat a feature of the city 221 is not the same type of information asthe geolocation information provided by a city name 222, a streetaddress 223 or a precise latitude and longitude 224. Unlike a city name,which is a less precise description of a geolocation than a streetaddress, a feature of the city 221 is not simply a lower resolutionmeasure of the mapped item of protected information.

The resource column of hierarchy 220 describes where each item ofinformation may be found. In some cases, an item of protectedinformation can be found on the mobile device. For example, a GPSenabled mobile device may have recent GPS coordinates stored on thedevice and available to an application. In some cases, an item ofprotected information is available to a mobile device through aninteraction with an external resource, such as resource system 130. Forexample, the street address or city name of the current location of amobile device may not be stored on the device. In this case, the streetaddress or city name of the current location of a mobile device may bemade available to the mobile device through an interaction between themobile device and one or more resource systems; for example, a mobiledevice may submit a request for information to a resource system 130including the precise latitude and longitude of the mobile device'spresent location and the resource system 130 may make the streetaddress, the name of the city, or both available to the mobile device.In this example, an item of protected information was used to accessanother item of protected information from an external resource. In somecases, one or more items of protected information may be used to accessone or more items of protected information, one or more items ofnon-unique sharable information or combinations thereof.

In the list of mapped features of FIG. 2a , the sharable feature is anadjective descriptive of a geographic region. In other examples, thesharable feature may comprise a score or probability. For example, FIG.2c illustrates an example of a list of mapped features where thesharable feature is accompanied by a score. FIG. 2d illustrates anexample of a list of mapped features where the sharable features areaccompanied by a range (50,000-75,000) or categorization (“high”,“low”).

A geographic location may be associated with a mobile device. Forexample, a geographic location may describe a past, current or predictedfuture location of the mobile device. In an example, a geographiclocation may be a position, set of positions, boundary or a region. Ageographic location may be described in various ways such as, but notlimited to, using absolute coordinates (such as a latitude and alongitude), relative coordinates, a street address, a a direct marketarea (DMA) code, a zip code, an areal boundary, a perimeter, a boundary,one or more lines or combinations thereof. In an example, a geographiclocation can be a unique place name such as the name of a specificNational Park, the unique name of a shopping mall, or a uniquelydescriptive name of a store. In some cases, the geographic location canfurther comprise information related to an elevation or altitude. Forexample, elevation information may be useful for distinguishing a mobiledevice operator's location inside a multi-story building and enableconfiguration of elevation-specific mapped features.

For an item of protected information comprising a geographic location, amatching criterion may comprise a disposition describing the relativeposition of a geographic location with respect to a geographic location.Referring to FIG. 2a , the item of protected information is the currentlocation of the mobile device, the matching criterion is “inside one ofthe listed cities”; in this example, the disposition is “inside”.Referring to FIG. 2c , the item of protected information is the currentlocation of the mobile device, the matching criterion is “within 5 milesof one of the listed positions”; in this example, the disposition is“within 5 miles”.

In an example, a sharable feature may characterize an operator of amobile device, a mobile device, a mobile application status,interactions with a mobile application, a past, present or predictedfuture location associated with a mobile device or combinations thereof.For example, a sharable feature may comprise sociographic, psychographicor demographic information such as the likelihood that a mobile deviceoperator has an annual income of over $100,000. This type of sharablefeature may be mapped to geographic locations using data such as censusdata. For example, a mobile device operator in the city of BeverleyHills may have a higher probability of making over $100,000 a year thana mobile device operator at any flea market. A feature may characterizethe likely past, current or future behaviors of the operator of themobile device. For example, it may be known that residents of somestates check the weather report on their mobile devices with a higherfrequency than residents of other states; in this case, the behavior“high frequency of checking the weather forecast from a mobile device”may be a sharable feature which can be mapped to some geographiclocations, but not others.

A feature analysis system 100 may have access to a large number ofmapped features The feature analysis system 100 carefully selects asecure set of mapped features for provision to a mobile device such thatno combination of mapped features in the secure set is unique to anindividual item of protected information. For example, before sending aset of mapped features to a mobile device comprising the three lists(200, 230 and 240) of mapped features illustrated in FIG. 2a and FIG. 2cand FIG. 2d , the feature analysis system must analyze the mappedfeatures to ensure that the mobile device cannot accidentally disclosean item of protected information (the mobile device location) byproviding a response including two or three sharable features whichuniquely identify an individual location. List 200 describes ageographic location in terms of a city name; list 230 describes ageographic location in terms of a distance from a position; list 240describes a geographic location in terms of a distance from a boundary.In the example of a secure set comprising lists 200, 230 and 240, thefeature analysis system must analyze disparate geographic locationspecifications in the course of determining that no combination ofmapped features in the set is unique to a geographic location.

In some cases, two or more secure sets of mapped features may beprovided to a mobile device in sequence. To protect the privacy of themobile device operator, the feature analysis system 100 may carefullyselect a new secure set of mapped features for provision to a particularmobile device such that no combination of mapped features from thecombination of the new secure set and one or more secure sets offeatures previously provided to the particular mobile device are uniqueto an item of protected information. In some embodiments, the history ofmapped features provided to a mobile device, or some portion thereof,may be maintained at the feature analysis system 100.

In some cases, an item of protected information may comprise ageographic location. To provide additional consumer privacy, a featureanalysis system 100 may take care to carefully select the mappedfeatures for a secure set of mapped features such that no combination ofmapped features in the secure set is unique to a pair of contiguouslocations. In this example, two nearby geographic locations, such as twoadjacent neighborhoods or two adjacent cities may have one or moreunusual features in common, so that sharing the one or more unusualfeatures is tantamount to disclosing that the geographic location islimited to a small, localized pair of contiguous geographic locations.In an embodiment, the feature analysis system may be configured tocarefully select the mapped features for a secure set of mapped featuressuch that no combination of mapped features in the secure set is uniqueto a configurable number of two or more contiguous locations.

In use, some mobile devices such as cell phones may be subject tofrequent location changes. For mapped features with ageolocation-related item of protected information, the passage of timeconfers some privacy protection. For example, with respect to the samesecure set of mapped features, the set of sharable features receivedfrom a mobile phone at one point in time may be different at a secondpoint in time due to a location change. For this reason, some degree ofprivacy may be maintained even when a comprehensive history of mappedfeatures provided to a mobile device is not maintained at the featureanalysis system 100.

The mapping of some sharable features to some items of protectedinformation may be subject to frequent change. For example, a mappingbetween an item of protected information, such as an exact geolocation,and a sharable feature such as the crowd density value (low, medium,high) may vary with respect to the time-of-day. For example, at the foodcourt inside a mall, the crowd density may be “high” at lunchtime and“low” at midnight, for days the mall is open. In another example, thelikelihood that the mobile device operator is between the ages of 18 and25 at a particular location may be sensitive to the season; for example,the likelihood of a mobile device operator being between the ages of 18and 25 may be high during spring break at a popular beach resort, butlower during other times of the year.

In an example, the variability of sharable features over time may bemanaged by altering the mappings over time (map Daytona Beach and “high”likelihood of the mobile device operator being between the ages of 18-25during spring break, but change the mapping to reflect “moderate”likelihood at other times of the year.) There are a variety of otherways to manage this. In another example, a resource system 130 may beused to keep track of this type of variability. For example, a resourcesystem 130 may receive a request for information from mobile devicewhich includes the name of the mobile device's location and returns avalue representing the likelihood that the mobile device operator isbetween the ages of 18 and 25; the mobile device may subsequentlyprovide the sharable feature (the value of the likelihood that themobile device operator is between the ages of 18 and 25) to the featureanalysis system 100.

Privacy compliance instructions are provided to the mobile device, forexecution at the mobile device. In an embodiment, instruction manager108 provides privacy compliance instructions for integration with amobile application 122 or an operating system. Privacy complianceinstructions which are integrated with a mobile application are executedon the mobile device and may be invoked by the mobile application or anoperating system. In an example, the privacy compliance instructions canbe provided to a mobile application developer in the form of a softwaredevelopers kit (SDK) which can be integrated with a mobile applicationand then distributed for installation and execution on a mobile device.In some cases, the mobile application developer will distribute theprivacy compliance instructions in conjunction with their mobileapplication for installation and execution on the mobile device. In somecases, a mobile device may have multiple mobile applications and eachmobile application may operate its own set of privacy complianceinstructions.

In an embodiment, the privacy compliance instructions may comprise ascript. In an example, the script may be distributed to the mobiledevice in conjunction with an operating system, a software applicationor as part of a software application update. In an example, the script,or portions thereof, may be distributed over a network 150 to the mobiledevice 120.

Privacy compliance instructions are configured to enable the mobiledevice to receive a secure set of mapped features from the analysissystem 100 and select a mapped feature by identifying an item ofprotected information available to the mobile device which correspondsto a matching criterion found in the received set of mapped features. Aspreviously discussed, an item of protected information may be directlyavailable to the mobile device, meaning it is available onboard themobile device, such as an item which is stored on the mobile device; anitem of information may be available to the mobile device through aninteraction between the mobile device and software or hardware which isexternal to the mobile device, such as external resource system 140.

Identifying an item of protected information which corresponds to amatching criterion found in the received set of mapped features may meandetermining if there is an exact match between a matching criterion andan item of protected information available to the mobile device. Forexample, if the item of protected information is the mobile device'sexact geolocation, and the matching criterion is “any exact geolocationin Redondo Beach, Manhattan Beach or San Diego”, the mobile device'sexact location will only correspond to the matching criterion if themobile device's geolocation is inside Redondo Bean, Manhattan Beach ofSan Diego, and meets the matching criterion exactly.

In an example, correspondence may be determined based on an inexact,approximate or probabilistic match. In an embodiment, the privacycompliance instructions may comprise instructions to evaluate one ormore items of protected information available to the mobile device. Forexample, the privacy compliance instructions may include instructionsfor rating, scoring or ranking one or more items of protectedinformation individually or collectively. In some cases, such a rating,score or rank may be used to determine if an item of protectedinformation available to the mobile device corresponds to the matchingcriterion. In some cases, such a rating, score or rank may be sent to aprivacy compliant destination such as feature analysis system 100,privacy compliant system 140 or both. In some cases, a rating, score orrank may be used at the mobile device to identify if one or more itemsof protected information correspond to the matching criterion based onan inexact, approximate or probabilistic match.

The privacy compliance instructions direct the mobile device to identifythe sharable feature of the selected mapped feature and send thesharable feature to one or more privacy compliant destinations, such asthe feature analysis system 100, privacy compliant system 140, orcombinations thereof. In an example, the sharable feature may be sent toa privacy compliant destination in conjunction with one or moreidentifiers, such as a software identifier, a hardware identifier orcombinations thereof. In some cases, the sharable feature may be sent toa privacy compliant destination in conjunction with a non-uniqueidentifier, such as a cohort identifier common to a cohort of devices,applications or both. At the feature analysis system 100, informationsuch as the received sharable feature may be used to perform subsequentanalysis and actions such as, but not limited to, the selection ofcustomized content for the mobile device, the selection of anadvertisement for delivery to the mobile device, reporting and modeling.Similarly, additional information, such as a rating score or rank may besent to the feature analysis system 100 and used to perform subsequentanalysis and actions. Advantageously, the feature analysis system 100protects the privacy of the mobile device user because it does notrequire the collection of protected information for the subsequentanalysis or actions.

In an embodiment, the connectivity of the mobile device 120 may be takeninto consideration before any data transmission to the mobile device isinitiated by the feature analysis system 100. For example, in somecases, the feature analysis system 100 may refrain from transmittingdata, such as the secure set of mapped features or any portion thereof,unless the mobile device 120 is coupled to a wireless network (“wi-fi”)to avoid cellular phone data charges. In some cases, the featureanalysis system 100 may adjust the size of the secure set of mappedfeatures sent to the mobile device 120 based on one or more criteriasuch as the mobile device's bandwidth availability, the mobile device'smemory availability, transmission costs, or combinations thereof.Similarly, the privacy compliant instructions may instruct the mobiledevice 120 to refrain from transmitting data, such as informationrequests to a resource system 130, sharable features to a featureanalysis system 100, or both, unless the mobile device 120 is coupled toa wi-fi network.

In some cases, sequentially providing multiple small secure sets ofmapped features to a mobile device instead of providing a large secureset of mapped features, allows the analysis system to pace resourceusage. This technique can be used to avoid consuming too much networkbandwidth and too much memory, power and processing resources at themobile device at any given time.

In an embodiment, the privacy compliance instructions can includeinstructions to repeat the process of identifying one or more sharablefeatures using the matching criterion found in a previously receivedsecure set of mapped features. In some cases, repeating the process caninclude determining a score, rating or rank for one or more sharablefeatures, either individually or collectively. A change in the updatedsharable features may reflect movement of the mobile device 120, thetime-dependent nature of a mapped feature, a change in the mobileapplication status, a change in the mobile device status or combinationsthereof.

In some cases, new secure set of mapped features may be sent to a mobiledevice. As a result, new sharable features may be selected and sent tothe mobile device for assessment, providing the analysis system with aview of the relevant features associated with a mobile device. In somecases, this may provide an increasingly detailed view of a mobiledevice, mobile device operator or mobile application status. However,because mobile devices may be in motion, sequential analysis introducesthe possibility that sequential analyses are not executed with respectto the same location. Sharable features characteristic of one locationmay not be relevant to another location. Furthermore, the matchingcriterion for an item of protected information may change as a functionof time, time of day or seasonality. For these reasons, sequentialanalysis may provide an updated set of sharable features or a new set ofsharable features, however it does not necessarily provide anincreasingly detailed view. Advantageously, the mobility of the devices,and the dynamic nature of the aforementioned relationships may introducesome degree of ambiguity into information which may be collected by aprivacy compliant destination, and may provide an additional layer ofprivacy for the mobile device operator.

In some cases, the selection of a new secure set of mapped features maybe based on the sharable features which are useful for a particularanalysis or advertising campaign. In some cases, the selection may bebased on the activity level of applications running on the mobile deviceor other information related to the hardware on the mobile device, thesoftware on the mobile device, the engagement level of the operator ofthe mobile device, the media consumption history of the mobile device,the gross geographic location of the mobile device, or combinationsthereof.

Sharable features received from the mobile device may be analyzed for avariety of purposes. For example, the mobile device or mobile deviceapplication may be selected to receive custom content or advertisingcontent based on the presence or absence of one or more features in therelevant subset. In some cases, sharable features may be aggregated forsets of mobile device operators or a community of mobile applicationusers, enabling the generation of sharable feature-related reporting.For example, the sharable features of all of the users of a mobileapplication may be aggregated to create a feature profile of that mobileapplication's users. The installation base of a mobile application maybe broken out in a variety of ways, such as by usage style, engagement,the execution of one or more actions such as upgrading a service relatedto the mobile application or purchasing an item through the mobileapplication, and the sharable features of each subset of the mobileapplication's users may be aggregated to form a feature profile.Similarly, the sharable features of all of the users of a particulartype of mobile device may be aggregated to create a feature profile forthe owners of that device.

After sharable features are received from the mobile device at thefeature analysis system 100, the sharable features may be groupedtogether into feature histories for analysis. For example, where therean identifier, such as an identifier for a mobile device, is permitted,a feature history for each respective mobile device may be created andmaintained. Similarly, a feature history may be established for anindividual instance of an installed mobile application, a set ofapplications running on an individual mobile device or a browser runningon a mobile device. A feature history may be used to selectadvertisements for delivery to a mobile device or to select customcontent for delivery to a mobile device. In some cases, sharablefeatures may be received in conjunction with a non-unique identifier. Afeature history may be created and maintained per non-unique identifier,enabling privacy-preserving cohort analysis and reporting at the featureanalysis system.

FIG. 3a illustrates an example of possible interactions between afeature analysis system 100, a mobile device 120 and a privacy compliantsystem 140, according to an embodiment. In this example, privacycompliance instructions are provided by the feature analysis system tothe mobile device 120 (301). Some or all of the privacy complianceinstructions may be provided directly from the feature analysis system100 to the mobile device 120, indirectly through a third-party such asan application vendor, or combinations thereof.

A secure set of mapped features are selected for provision to the mobiledevice 120 by the feature analysis system 100 and sent to the mobiledevice 120 (302). One or more of the mapped features may be constructedat the feature analysis system 100.

Mobile device 120 executes the privacy compliance instructions andselects one or more sharable features by identifying an item ofprotected information available to the mobile device which correspondsto a matching criterion found in the received secure set of mappedfeatures. In some cases, the mobile device may generate a score, ratingor ranking based at least in part on an item of protected informationavailable to the mobile device. Instructions used for generating ascore, rating or ranking may be provided to the mobile device 120 by thefeature analysis system 100 in conjunction with the secure set of mappedfeatures, the privacy compliance instructions or both.

Mobile device 120 sends one or more sharable features to a privacycompliant destination, such as the feature analysis system 100 (303 a),the privacy compliant system 140 (303 b) or both. In some cases,additional information, such as a score, rating or ranking may be sentfrom the mobile device 120 in conjunction with the sharable feature. Insome cases, an identifier may be sent from the mobile device 120 inconjunction with one or more sharable features.

The privacy compliance instructions may direct the mobile device 120 tore-assess a previously received secure set of mapped features. In somecases, re-assessment may be initiated according to a schedule, triggeredby a signal received from an external resource such as the featureanalysis system 100, triggered by a change in state of the mobile deviceor mobile application or combinations thereof. The mobile device selectsone or more sharable features by identifying an item of protectedinformation available to the mobile device which corresponds to amatching criterion found in the previously received secure set of mappedfeatures. A set of updated sharable features may be sent to the featureanalysis system 100 (304 a), a privacy compliant system (304 b) or both.

A new secure set of mapped features are selected for provision to themobile device 120 by the feature analysis system 100 and sent to themobile device 120 (305). In some cases, the new secure set of mappedfeatures may be carefully selected, so that, subject to matchingcriteria from both the previously sent secure set of mapped features andthe new secure set of mapped features, no combination of sharablefeatures map to a unique item of protected information. In some cases,the new secure set of mapped features may be configured and/or selectedbased on a previously received sharable feature, the requirements of anadvertising campaign, or both.

FIG. 3b illustrates an example of possible interactions between afeature analysis system 100, a mobile device 120, a resource system 130and a privacy compliant system 140, according to an embodiment. In thisexample, privacy compliance instructions are provided by the featureanalysis system to the mobile device 120 (311). As previously discussed,some or all of the privacy compliance instructions may be provideddirectly from the feature analysis system 100 to the mobile device 120,indirectly through a third-party such as an application vendor, orcombinations thereof. The privacy compliance instructions may or may notinclude a partial or complete hierarchy of information which may be usedby the mobile device 120 to request information from a resource system.For example, a request for information may be sent to a resource system,and may include information stored on the mobile device.

A secure set of mapped features are selected for provision to the mobiledevice 120 by the feature analysis system 100 and sent to the mobiledevice 120 (312). In some cases a partial or complete hierarchy ofinformation may be sent to the mobile device 120, in conjunction withthe secure set of mapped features.

Mobile device 120 executes the privacy compliance instructions. As aresult, a request for information is sent to a resource system 130(313). The request for information may include information such as oneor more items of protected information available to the mobile device120. The privacy compliance instructions direct the mobile device toselects one or more sharable features by identifying an item ofprotected information available to the mobile device which correspondsto a matching criterion found in the received secure set of mappedfeatures. In some cases, the item of protected information described ina mapped feature may be available to the mobile device through aninteraction with an external resource, such as a resource system 130.The hierarchy can be used by the mobile device to configure a requestfor information, and may include details such as where to submit arequest for information and how to format the request.

Resource system 130 receives the request for information, including oneor more items of information such as an item of protected information,and sends information back to the mobile device 120 (314). In somecases, the information sent back to the mobile device 120 may comprisean item of protected information. In some cases, the information sentback to the mobile device 120 may comprise a sharable feature. In anexample, the resource system 130 receives an exact geolocation from themobile device 120 and returns a street address. In another example, theresource system 130 receives an exact geolocation from a mobile device120 and returns the minimum driving time between the exact geolocationand the closest storefront of a chain of coffee shops under currenttraffic conditions.

Mobile device 120 receives the information from the resource system 130and uses this information to select one or more sharable features byidentifying an item of protected information available to the mobiledevice which corresponds to a matching criterion found in the receivedsecure set of mapped features. The mobile device 120 sends one or moresharable features to a privacy compliant destination, such as thefeature analysis system 100 (315 a), the privacy compliant system 140(315 b) or both. In some cases, additional information, such as a score,rating or ranking may be sent from the mobile device 120 in conjunctionwith a sharable feature. In some cases, an identifier may be sent fromthe mobile device 120 in conjunction with one or more sharable features.

FIG. 4 is a high-level block diagram illustrating an example of acomputer 400 for use as a feature analysis system 100, a mobile device120, a resource system 130 and/or a privacy compliant system 140 of FIG.1, FIG. 3a and FIG. 3b , in accordance with an embodiment of theinvention. Illustrated are at least one processor 402 coupled to achipset 404. The chipset 404 includes a memory controller hub 450 and aninput/output (I/O) controller hub 455. A memory 406 and a graphicsadapter 413 are coupled to the memory controller hub 450, and a displaydevice 418 is coupled to the graphics adapter 413. A storage device 408,keyboard 410, pointing device 414, and network adapter 416 are coupledto the I/O controller hub 455. Other embodiments of the computer 400have different architectures. For example, the memory 406 is directlycoupled to the processor 402 in some embodiments.

The storage device 408 is a computer-readable storage medium such as ahard drive, compact disk read-only memory (CD-ROM), DVD, or asolid-state memory device. The memory 406 holds instructions and dataused by the processor 402. The pointing device 414 is used incombination with the keyboard 410 to input data into the computer system400. Mechanisms used to convey user input can include, but are notlimited to, touchscreen interfaces, touchpads, directional pointingdevices, voice controlled interfaces, hardware keyboard shortcuts,directional hardware keys and hardware elements such as wheels androlling balls. The graphics adapter 413 displays images and otherinformation on the display device 418. In some embodiments, the displaydevice 418 includes a touch screen capability for receiving user inputand selections. The network adapter 416 couples the computer system 400to the communications network 101. Some embodiments of the computer 400have different and/or other components than those shown in FIG. 4.

The computer 400 is adapted to execute computer program modules forproviding functionality described herein. As used herein, the term“module” refers to computer program instructions and other logic used toprovide the specified functionality. Thus, a module can be implementedin hardware, firmware, and/or software. In one embodiment, programmodules formed of executable computer program instructions are stored onthe storage device 408, loaded into the memory 406, and executed by theprocessor 402.

The types of computers 400 used by the entities of FIG. 1 and FIG. 3aand FIG. 3b can vary depending upon the embodiment and the processingpower used by the entity. For example, a mobile device 110 that is cellphone or PDA typically has limited processing power, a small display418, and might lack a pointing device 414. The feature analysis system100, in contrast, may comprise multiple blade servers working togetherto provide the functionality described herein.

FIG. 5 is a flow chart illustrating an example of a method 500 forfeature analysis, according to an embodiment. Referring to Step 510,privacy compliance instructions are provided to the mobile device 120.

Referring to Step 520, a secure set of mapped features is selected bythe feature analysis system 100.

Referring to Step 530, the secure set of mapped features is sent to themobile device 120.

Referring to Step 540, the privacy compliance instructions are executedat the mobile device 120. This causes the mobile device to select one ormore sharable features by identifying an item of protected informationavailable to the mobile device which corresponds to a matching criterionfound in the received secure set of mapped features. One or moresharable features of the selected mapped feature are identified. One ormore sharable features are sent to a privacy compliant destination, suchas the feature analysis system 100, a privacy compliant system 140 orcombinations thereof.

Referring to Step 550, one or more sharable features are received by theanalysis system 100 and analyzed for subsequent action.

FIG. 6 is a flow chart illustrating an example of a method 600 enablingfeature analysis for mobile devices, according to an embodiment.Referring to Step 610, privacy compliance instructions are received by amobile device 120.

Referring to Step 620, a secure set of mapped features is received at amobile device 120 from a feature analysis system 100.

Referring to Step 630, a mapped feature from a received secure set ofmapped features is selected by identifying an item of protectedinformation available to the mobile device which corresponds to amatching criterion found in the received secure set of mapped features.

Referring to Step 640, a sharable feature of the selected mapped featureis identified.

Referring to Step 650, a sharable feature of the selected mapped featureis sent to a privacy compliant destination.

The order of the steps in the foregoing described methods of theinvention are not intended to limit the invention; the steps may berearranged.

Foregoing described embodiments of the invention are provided asillustrations and descriptions. They are not intended to limit theinvention to precise form described. In particular, it is contemplatedthat functional implementation of invention described herein may beimplemented equivalently in hardware, software, firmware, and/or otheravailable functional components or building blocks, and that networksmay be wired, wireless, or a combination of wired and wireless. Othervariations and embodiments are possible in light of above teachings, andit is thus intended that the scope of invention not be limited by thisDetailed Description, but rather by Claims following.

What is claimed is:
 1. A computer-implemented method for featureanalysis, the method comprising: providing a plurality of mappedfeatures to a mobile device, each mapped feature comprising an item ofprotected information mapped to a non-unique sharable feature; andproviding privacy compliance instructions to the mobile device forexecution at the mobile device wherein the privacy complianceinstructions comprise instructions for: generating a score for at leastone item of protected information available to the mobile device;matching the at least one item of protected information available to themobile device to a mapped feature from the provided plurality of mappedfeatures, the matching mapped feature's item of protected informationmatching the item of protected information available to the mobiledevice; and sending the matching mapped feature's generated score. 2.The method of claim 1 wherein the privacy compliance instructionsfurther comprise instructions for: sending the matching mapped feature'snon-unique sharable feature.
 3. The method of claim 1 furthercomprising: sending an identifier.
 4. The method of claim 1 wherein:generating the score comprises generating a collective scorecharacterizing at least two items of protected information available tothe mobile device.
 5. The method of claim 1 wherein: the generated scorecomprises a category.
 6. The method of claim 1 wherein: the generatedscore comprises a range.
 7. The method of claim 1 further comprising:receiving the matching mapped feature's score from the mobile device;selecting content based on the matching mapped feature's score; andsending the selected content to the mobile device.
 8. The method ofclaim 1 further comprising: receiving the matching mapped feature'sscore from the mobile device; customizing content based on the matchingmapped feature's score; and sending the customized content to the mobiledevice.
 9. The method of claim 1 wherein: at least one mapped featuremaps an item of protected information comprising a geographic locationto a non-unique sharable feature which is not a geographic location. 10.The method of claim 1 wherein: at least one item of protectedinformation comprises a geographic location described in relativecoordinates.
 11. The method of claim 1 wherein: the matching mappedfeature is a result of a match between at least one item of protectedinformation available to the mobile device and a mapped feature, thematch selected from the group consisting of: an inexact match, anapproximate match and a probabilistic match.
 12. A non-transitorycomputer readable medium with computer executable instructions storedthereon executed by a processor to perform a method, the methodcomprising: providing a plurality of mapped features to a mobile device,each mapped feature comprising an item of protected information mappedto a non-unique sharable feature; and providing privacy complianceinstructions to the mobile device for execution at the mobile devicewherein the privacy compliance instructions comprise instructions for:generating a score for at least one item of protected informationavailable to the mobile device; matching the at least one item ofprotected information available to the mobile device to a mapped featurefrom the provided plurality of mapped features, the matching mappedfeature's item of protected information matching the item of protectedinformation available to the mobile device; and sending the matchingmapped feature's generated score.
 13. The medium of claim 12 wherein:the generated score comprises a category.
 14. The medium of claim 12wherein: the generated score comprises a range.
 15. The medium of claim12, the method further comprising: receiving the matching mappedfeature's score from the mobile device; selecting content based on thematching mapped feature's score; and sending the selected content to themobile device.
 16. The medium of claim 12, the method furthercomprising: receiving the matching mapped feature's score from themobile device; customizing content based on the matching mappedfeature's score; and sending the customized content to the mobiledevice.
 17. The medium of claim 12 wherein: at least one mapped featuremaps an item of protected information comprising a geographic locationto a non-unique sharable feature which is not a geographic location. 18.The medium of claim 12 wherein: at least one item of protectedinformation comprises a geographic location described in relativecoordinates.
 19. The medium of claim 12 wherein: the matching mappedfeature is a result of a match between at least one item of protectedinformation available to the mobile device and a mapped feature, thematch selected from the group consisting of: an inexact match, anapproximate match and a probabilistic match.
 20. A system comprising: aprocessor; and, a non-transitory computer readable storage mediumstoring processor-executable computer program instructions, theinstructions comprising instructions for: providing a plurality ofmapped features to a mobile device, each mapped feature comprising anitem of protected information mapped to a non-unique sharable feature;and providing privacy compliance instructions to the mobile device forexecution at the mobile device wherein the privacy complianceinstructions comprise instructions for: generating a score for at leastone item of protected information available to the mobile device;matching the at least one item of protected information available to themobile device to a mapped feature from the provided plurality of mappedfeatures, the matching mapped feature's item of protected informationmatching the item of protected information available to the mobiledevice; and sending the matching mapped feature's generated score.